Christopher Lynn
We are interested in understanding how structure and function emerge in complex living systems, particularly the brain. We study how individual neurons interact to form networks; how whole-brain activity orchestrates cognition; and how humans communicate to process information. We approach these problems from a statistical physics perspective: seeking to identify the key minimal ingredients that combine to produce collective macroscopic phenomena.
Chris Lynn joined Yale as an Assistant Professor of Physics, a member of the Quantitative Biology Institute, and a member of the Wu Tsai Institute. Chris received his undergraduate degrees in Physics and Mathematics in 2014 from Swarthmore College and his Ph.D. in 2020 from the University of Pennsylvania. As a graduate student in Dani Bassett’s lab, he used network science and information theory to study human information processing in cognitive experiments. Chris then completed his postdoc as a James S. McDonnell Foundation Fellow at Princeton, working closely with William Bialek, Stephanie Palmer, and David Schwab to understand the emergence of irreversibility and complex structures in neural systems.
We are interested in understanding how structure and function emerge in complex living systems, particularly the brain. We study how individual neurons interact to form networks; how whole-brain activity orchestrates cognition; and how humans communicate to process information. We approach these problems from a statistical physics perspective: seeking to identify the key minimal ingredients that combine to produce collective macroscopic phenomena.
- Christopher W. Lynn & Dani S. Bassett. “The physics of brain network structure, function, and control”. Nat. Rev. Phys. (2019).
- Christopher W. Lynn, Eli J. Cornblath, Lia Papadopoulos, Maxwell A. Bertolero, & Dani S. Bassett. “Broken detailed balance and entropy production in the human brain”. PNAS (2021).
- Christopher W. Lynn, Caroline M. Holmes, William Bialek, & David J. Schwab. “Decomposing the local arrow of time in interacting systems”. Phys. Rev. Lett. (2022).
- Christopher W. Lynn, Caroline M. Holmes, & Stephanie E. Palmer. “Heavy-tailed neuronal connectivity arises from Hebbian self-organization”. Preprint.
- Christopher W. Lynn, Ari E. Kahn, Nathaniel Nyema, & Dani S. Bassett. “Abstract representations of events arise from mental errors in learning and memory”. Nat. Commun. (2020).
- Christopher W. Lynn, Lia Papadopoulos, Ari E. Kahn, & Dani S. Bassett. “Human information processing in complex networks”. Nat. Phys. (2020).
- Christopher W. Lynn & Dani S. Bassett. “Quantifying the compressibility of complex networks”. PNAS (2021).